Introduction

An index of brain white matter fiber density was created with this patent to significantly increase the prediction of survival in glioblastoma (GBM), the most common malignant tumor of the brain, in accordance with the assumption that the prognosis of GBM is not independent of the structural organization of the brain.

Technical features

Although neuroimaging data are indispensable for the diagnosis and surgical planning of brain tumors, in the field of GBM prognostic factors, there is no biomarker based on the spatial topology of the tumor in relation to the structural organization of the encephalon. The invention consists of the development of an index of indirect structural density estimation of brain white matter fiber disconnection induced by a particular tumor (average number of fibers, relative to a normative atlas, that cross each voxel–the 3D unit that encapsulates the brain scanner signal, analogous to 2D pixels of digital screens–for each tumor, depending on its location) that can be measured by magnetic resonance imaging (MRI) scattering techniques. This index shows promise for wide use in clinical practice and clinical trials and could be used as a biomarker for evaluation of new therapies (clinical trials) making patient prognosis more accurate. TRL 4

Possible Applications

– Software sales to be distributed to third-party MRI machine manufacturers;
– Integration of all analysis and preprocessing steps into final product;
– Cloud managed/integrated by universities or start-ups (clinical trial companies, researchers, clinicians).

Advantages

– Ease of application on MRI data acquired in the clinical setting;
– Possible development of end-to-end methodology: from examination (clinical MRI) to survival estimation via one step;
– Ease of use even for clinicians and researchers unfamiliar with programming languages;
– Patient prognosis more accurate even before surgery and possible customization of the methodology to offer different normative atlases with data-specific characteristics (e.g., stratifying the dataset by age, gender, or geographic origin);